User Manual



This document is the recommended first read if you are interested in using Nuitka, understand its use cases, check what you can expect, license, requirements, credits, etc.

Nuitka is the Python compiler. It is a seamless replacement or extension to the Python interpreter and compiles every construct that CPython 2.6, 2.7, 3.2, 3.3, 3.4, 3.5, and 3.6 have. It then executes uncompiled code, and compiled code together in an extremely compatible manner.

You can use all Python library modules or and all extension modules freely. It translates the Python into a C level program that then uses "libpython" to execute in the same way as CPython does. All optimization is aimed at avoiding overhead, where it's unnecessary. None is aimed at removing compatibility, although there is an "improved" mode, where not every bug of standard Python is emulated, e.g. more complete error messages are given.



  • C Compiler: You need a compiler with support for C11 or alternatively for C++03 [1]

    Currently this means, you need to use either of these compilers:

    • The ``gcc` compiler of at least version 5.1, or the g++ compiler of at least version 4.4 as an alternative.
    • The clang compiler on MacOS X or FreeBSD, based on LLVM version 3.2 or higher.
    • The MinGW64 [2] C11 compiler on Windows, ideally the one based on gcc 5.1 or higher. Or the C++ compiler of at least version 4.4 as an alternative.
    • Visual Studio 2017 or higher on Windows [3], older versions may work, but are not officially supported.
  • Python: Version 2.6, 2.7 or 3.2, 3.3, 3.4, 3.5, 3.6 (yes, but read below)

    Python3, yes but Python2 compile time dependency

    For Python3 you need a Python2, but only during the compile time only, and that is for Scons (which orchestrates the C compilation), and is not yet ported. So for Python 3.x, there is currently a requirement to also have a Python 2.x installed in order to compile, not to run a binary.

    Nuitka itself is fully Python3 compatible except for Scons.

    Moving to other machines

    The created binaries can be made executable independent of the Python installation, with --standalone option.

    Binary filename suffix ".exe" even on Linux

    The created binaries have an ".exe" suffix, that you are free to remove that and yes, they are still Linux binaries. The suffix is just to be sure that the original script name and the binary name do not collide.

    It has to be CPython or AnaConda

    You need the standard Python implementation, called "CPython", to execute Nuitka, because it is closely tied to using it.

    On Windows, the so called "WinPython" and "AnaConda" distributions work, but will cause issues for acceleration mode. Standalone mode and creating extension modules or packages will work. For acceleration mode, you need to copy the "PythonXX.DLL" alongside of it.

  • Operating System: Linux, FreeBSD, NetBSD, MacOS X, and Windows (32/64 bits).

    Others may work as well. The portability is expected to be generally good, but the e.g. Scons usage may have to be adapted.

  • Architectures: x86, x86_64 (amd64), and arm, likely more

    Other architectures are expected to also work, out of the box, as Nuitka is generally not using any hardware specifics. These are just the ones tested and known to be good. Feedback is welcome. Generally the architectures that Debian supports can be considered good and tested too.

[1] Support for this C11 is given with gcc 5 or higher or clang. The MSVC compiler doesn't do it yet. But as a workaround, the C++03 language standard is very overlapping with C11 and is used instead where the C compiler is too old. Nuitka used to require a C++ compiler in the past, but it changed.
[2] Download MinGW64 from here and choose 64 or 32 bits matching your Python. Use both MinGW64 and 64 bits Python if you have the choice of which Python to use. Install it to "C:MinGW64" or "MinGW64" (same disk root) to find it automatically.
[3] Download for free from (the Express editions will normally work just fine). The latest version is recommend. There is no need to use older versions.

Command Line

No environment variable changes are needed, most noteworthy, you do not have to mess with PYTHONPATH at all for Nuitka. You just execute the nuitka and nuitka-run scripts directly without any changes to the environment. You may want to add the bin directory to your PATH for your convenience, but that step is optional.

Nuitka has a --help option to output what it can do:

nuitka --help

The nuitka-run command is the same as nuitka, but with different default. It tries to compile and directly execute a Python script:

nuitka-run --help

These option that is different is --run, and passing on arguments after the first non-option to the created binary, so it is somewhat more similar to what plain python will do.


Nuitka is licensed under the Apache License, Version 2.0; you may not use it except in compliance with the License.

You may obtain a copy of the License at

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Use Cases

Use Case 1 - Program compilation with all modules embedded

If you want to compile a whole program recursively, and not only the single file that is the main program, do it like this:

nuitka --recurse-all


There are more fine grained controls than --recurse-all available. Consider the output of nuitka --help.

In case you have a plugin directory, i.e. one which cannot be found by recursing after normal import statements via the PYTHONPATH (which would be recommended way), you can always require that a given directory shall also be included in the executable:

nuitka --recurse-all --recurse-directory=plugin_dir


If you don't do any dynamic imports, simply setting your PYTHONPATH at compilation time will be sufficient for all your needs normally.

Use --recurse-directory only if you make __import__() calls that Nuitka cannot predict, because they e.g. depend on command line parameters. Nuitka also warns about these, and point to the option.


The resulting binary still depends on CPython and used C extension modules being installed.

If you want to be able to copy it to another machine, use --standalone and copy the created program.dist directory and execute the program.exe put inside.


The resulting filename will be program.exe on all platforms, that doesn't mean it doesn't run on non-Windows! But if you compile program we wouldn't want to overwrite it, or be unsure which one is the compiled form, and which one is not.

Use Case 2 - Extension Module compilation

If you want to compile a single extension module, all you have to do is this:

nuitka --module

The resulting file "" can then be used instead of "". It's left as an exercise to the reader, what happens if both are present.


The option --recurse-all and other variants work as well.

Use Case 3 - Package compilation

If you need to compile a whole package and embedded all modules, that is also feasible, use Nuitka like this:

nuitka --module some_package --recurse-directory=some_package


The recursion into the package directory needs to be provided manually, otherwise the package is empty. Data files located inside the package will not be embedded yet.

Where to go next

Remember, this project is not completed yet. Although the CPython test suite works near perfect, there is still more work needed, to make it do more optimization. Try it out.

Subscribe to its mailing lists

Please visit the mailing list page in order to subscribe the relatively low volume mailing list. All Nuitka issues can be discussed there. Also this is the place to stay informed of what's coming.

Report issues or bugs

Should you encounter any issues, bugs, or ideas, please visit the Nuitka bug tracker and report them.

Best practices for reporting bugs:

  • Please aways include the following information in your report, for the underlying Python version. You can easily copy&paste this into your report.

    nuitka --version
  • Try to make your example minimal. That is, try to remove code that does not contribute to the issue as much as possible. Ideally come up with a small reproducing program that illustrates the issue, using print with different results when that programs runs compiled or native.

  • If the problem occurs spuriously (i.e. not each time), try to set the environment variable PYTHONHASHSEED to 0, disabling hash randomization. If that makes the problem go away, try increasing in steps of 1 to a hash seed value that makes it happen every time.

  • Do not include the created code in your report. Given proper input, it's redundant, and it's not likely that I will look at it without the ability to change the Python or Nuitka source and re-run it.

Contact me via email with your questions

The best place to ask questions is the mailing list. You are welcome to contact me via email with your questions. But it is increasingly true that for user questions the mailing list is the best place to go. Often somebody there knows more about what you are doing.

Follow me on Twitter

Nuitka announcements and interesting stuff is pointed to on the Twitter account, but obviously with no details. @KayHayen.

Word of Warning

Consider using this software with caution. Even though many tests are applied before releases, things are potentially breaking. Your feedback and patches to Nuitka are very welcome.

Especially report it please, if you find that anything doesn't work, because the project is now at the stage that this should not happen and most definitely will mean you encountered an unknown bug.

Join Nuitka

You are more than welcome to join Nuitka development and help to complete the project in all minor and major ways.

The development of Nuitka occurs in git. We currently have these 3 branches:

  • master:

    This branch contains the stable release to which only hotfixes for bugs will be done. It is supposed to work at all times and is supported.

  • develop:

    This branch contains the ongoing development. It may at times contain little regressions, but also new features. On this branch the integration work is done, whereas new features might be developed on feature branches.

  • factory:

    This branch contains unfinished and incomplete work. It is very frequently subject git rebase and the public staging ground, where my work for develop branch lives first. It is intended for testing only and recommended to base any of your own development on. When updating it, you very often will get merge conflicts. Simply resolve those by doing git reset --hard origin/factory and switch to the latest version.


I accept patch files, git formatted patch queues (use git format-patch origin command), or if you prefer git pull on the social code platforms.

I will do the integration work. If you base your work on "master" or "develop" at any given time, I will do any re-basing required and keep your authorship intact.


The Developer Manual explains the coding rules, branching model used, with feature branches and hotfix releases, the Nuitka design and much more. Consider reading it to become a contributor. This document is intended for Nuitka users.


Should you feel that you cannot help Nuitka directly, but still want to support, please consider making a donation and help this way.

Unsupported functionality

The co_code attribute of code objects

The code objects are empty for for native compiled functions. There is no bytecode with Nuitka's compiled function objects, so there is no way to provide it.


Constant Folding

The most important form of optimization is the constant folding. This is when an operation can be fully predicted at compile time. Currently Nuitka does these for some built-ins (but not all yet, somebody to look at this more closely will be very welcome!), and it does it e.g. for binary/unary operations and comparisons.

Constants currently recognized:

5 + 6     # binary operations
not 7     # unary operations
5 < 6     # comparisons
range(3)  # built-ins

Literals are the one obvious source of constants, but also most likely other optimization steps like constant propagation or function inlining will be. So this one should not be underestimated and a very important step of successful optimizations. Every option to produce a constant may impact the generated code quality a lot.


The folding of constants is considered implemented, but it might be incomplete in that not all possible cases are caught. Please report it as a bug when you find an operation in Nuitka that has only constants as input and is not folded.

Constant Propagation

At the core of optimizations there is an attempt to determine values of variables at run time and predictions of assignments. It determines if their inputs are constants or of similar values. An expression, e.g. a module variable access, an expensive operation, may be constant across the module of the function scope and then there needs to be none, or no repeated module variable look-up.

Consider e.g. the module attribute __name__ which likely is only ever read, so its value could be predicted to a constant string known at compile time. This can then be used as input to the constant folding.

if __name__ == "__main__":
   # Your test code might be here


From modules attributes, only __name__ are currently actually optimized. Also possible would be at least __doc__. In the future, this may improve as SSA is expanded to module variables.

Built-in Name Lookups

Also built-in exception name references are optimized if they are used as module level read only variables:

except ValueError: # The ValueError is a slow global name lookup normally.


This works for all built-in names. When an assignment is done to such a name, or it's even local, then of course it is not done.

Built-in Call Prediction

For built-in calls like type, len, or range it is often possible to predict the result at compile time, esp. for constant inputs the resulting value often can be precomputed by Nuitka. It can simply determine the result or the raised exception and replace the built-in call with that value, allowing for more constant folding or code path reduction.

type("string") # predictable result, builtin type str.
len([1, 2])    # predictable result
range(3, 9, 2) # predictable result
range(3, 9, 0) # predictable exception, range raises due to 0.


The built-in call prediction is considered implemented. We can simply during compile time emulate the call and use its result or raised exception. But we may not cover all the built-ins there are yet.

Sometimes the result of a built-in should not be predicted when the result is big. A range() call e.g. may give too big values to include the result in the binary. Then it is not done.

range( 100000 ) # We do not want this one to be expanded


This is considered mostly implemented. Please file bugs for built-ins that are pre-computed, but should not be computed by Nuitka at compile time with specific values.

Conditional Statement Prediction

For conditional statements, some branches may not ever be taken, because of the conditions being possible to predict. In these cases, the branch not taken and the condition check is removed.

This can typically predict code like this:

if __name__ == "__main__":
   # Your test code might be here


if False:
   # Your deactivated code might be here

It will also benefit from constant propagations, or enable them because once some branches have been removed, other things may become more predictable, so this can trigger other optimization to become possible.

Every branch removed makes optimization more likely. With some code branches removed, access patterns may be more friendly. Imagine e.g. that a function is only called in a removed branch. It may be possible to remove it entirely, and that may have other consequences too.


This is considered implemented, but for the maximum benefit, more constants need to be determined at compile time.

Exception Propagation

For exceptions that are determined at compile time, there is an expression that will simply do raise the exception. These can be propagated upwards, collecting potentially "side effects", i.e. parts of expressions that were executed before it occurred, and still have to be executed.

Consider the following code:

print side_effect_having() + (1 / 0)
print something_else()

The (1 / 0) can be predicted to raise a ZeroDivisionError exception, which will be propagated through the + operation. That part is just Constant Propagation as normal.

The call side_effect_having()` will have to be retained though, but the print statement does not and can be turned into an explicit raise. The statement sequence can then be aborted and as such the something_else call needs no code generation or consideration anymore.

To that end, Nuitka works with a special node that raises an exception and is wrapped with a so called "side_effects" expression, but yet can be used in code as an expression having a value.


The propagation of exceptions is mostly implemented, but needs handling in every kind of operations, and not all of them might do it already. As work progresses or examples arise, the coverage will be extended. Feel free to generate bug reports with non-working examples.

Exception Scope Reduction

Consider the following code:

    b = 8
    print range(3, b, 0)
    print "Will not be executed"
except ValueError, e:
    print e

The try block is bigger than it needs to be. The statement b = 8 cannot cause a ValueError to be raised. As such it can be moved to outside the try without any risk.

b = 8
    print range(3, b, 0)
    print "Will not be executed"
except ValueError as e:
    print e


This is considered done. For every kind of operation, we trace if it may raise an exception. We do however not track properly yes, what can do a ValueError and what cannot.

Exception Block Inlining

With the exception propagation it is then becomes possible to transform this code:

    b = 8
    print range(3, b, 0)
    print "Will not be executed"
except ValueError, e:
    print e
    raise ValueError, "range() step argument must not be zero"
except ValueError, e:
    print e

Which then can be reduced by avoiding the raise and catch of the exception, making it:

e = ValueError( "range() step argument must not be zero" )
print e


This is not implemented yet.

Empty Branch Removal

For loops and conditional statements that contain only code without effect, it should be possible to remove the whole construct:

for i in range(1000):

The loop could be removed, at maximum it should be considered an assignment of variable i to 999 and no more.


This is not implemented yet, as it requires us to track iterators, and their side effects, as well as loop values, and exit conditions. Too much yet, but we will get there.

Another example:

if side_effect_free:

The condition check should be removed in this case, as its evaluation is not needed. It may be difficult to predict that side_effect_free has no side effects, but many times this might be possible.


This is considered implemented. The conditional statement nature is removed if both branches are empty, only the condition is evaluated, and checked for truth (in cases that could raise an exception).

Unpacking Prediction

When the length of the right hand side of an assignment to a sequence can be predicted, the unpacking can be replaced with multiple assignments.

a, b, c = 1, side_effect_free(), 3
a = 1
b = side_effect_free()
c = 3

This is of course only really safe if the left hand side cannot raise an exception while building the assignment targets.

We do this now, but only for constants, because we currently have no ability to predict if an expression can raise an exception or not.


Not implemented yet. Will need us to see through the unpacking of what is an iteration over a tuple, we created ourselves. We are not there yet, but we will get there.

Built-in Type Inference

When a construct like in xrange() or in range() is used, it is possible to know what the iteration does and represent that, so that iterator users can use that instead.

I consider that:

for i in xrange(1000):

could translate xrange(1000) into an object of a special class that does the integer looping more efficiently. In case i is only assigned from there, this could be a nice case for a dedicated class.


Future work, not even started.

Quicker Function Calls

Functions are structured so that their parameter parsing and tp_call interface is separate from the actual function code. This way the call can be optimized away. One problem is that the evaluation order can differ.

def f(a, b, c):
    return a, b, c

f(c = get1(), b = get2(), a = get3())

This will have to evaluate first get1(), then get2() and only then get3() and then make the function call with these values.

Therefore it will be necessary to have a staging of the parameters before making the actual call, to avoid an re-ordering of the calls to get1(), get2(), and get3().


Not even started. A re-formulation that avoids the dictionary to call the function, and instead uses temporary variables appears to be relatively straight forward once we do that kind of parameter analysis.

Lowering of iterated Container Types

In some cases, accesses to list constants can become tuple constants instead.

Consider that:

for x in [a, b, c]:

Can be optimized into this:

for x in (a, b, c):

This allows for simpler, faster code to be generated, and less checks needed, because e.g. the tuple is clearly immutable, whereas the list needs a check to assert that. This is also possible for sets.


Implemented, even works for non-constants. Needs other optimization to become generally useful, and will itself help other optimization to become possible. This allows us to e.g. only treat iteration over tuples, and not care about sets.

In theory something similar is also possible for dict. For the later it will be non-trivial though to maintain the order of execution without temporary values introduced. The same thing is done for pure constants of these types, they change to tuple values when iterated.


Contributors to Nuitka

Thanks go to these individuals for their much valued contributions to Nuitka. Contributors have the license to use Nuitka for their own code even if Closed Source.

The order is sorted by time.

  • Li Xuan Ji: Contributed patches for general portability issue and enhancements to the environment variable settings.
  • Nicolas Dumazet: Found and fixed reference counting issues, import packages work, improved some of the English and generally made good code contributions all over the place, solved code generation TODOs, did tree building cleanups, core stuff.
  • Khalid Abu Bakr: Submitted patches for his work to support MinGW and Windows, debugged the issues, and helped me to get cross compile with MinGW from Linux to Windows. This was quite a difficult stuff.
  • Liu Zhenhai: Submitted patches for Windows support, making the inline Scons copy actually work on Windows as well. Also reported import related bugs, and generally helped me make the Windows port more usable through his testing and information.
  • Christopher Tott: Submitted patches for Windows, and general as well as structural cleanups.
  • Pete Hunt: Submitted patches for MacOS X support.
  • "ownssh": Submitted patches for built-ins module guarding, and made massive efforts to make high quality bug reports. Also the initial "standalone" mode implementation was created by him.
  • Juan Carlos Paco: Submitted cleanup patches, creator of the Nuitka GUI, creator of the Ninja IDE plugin for Nuitka.
  • "dr. Equivalent": Submitted the Nuitka Logo.
  • Johan Holmberg: Submitted patch for Python3 support on MacOS X.
  • Umbra: Submitted patches to make the Windows port more usable, adding user provided application icons, as well as MSVC support for large constants and console applications.
  • David Cortesi: Submitted patches and test cases to make MacOS port more usable, specifically for the Python3 standalone support of Qt.

Projects used by Nuitka

  • The CPython project

    Thanks for giving us CPython, which is the base of Nuitka. We are nothing without it.

  • The GCC project

    Thanks for not only the best compiler suite, but also thanks for supporting C++11 which helped to get Nuitka off the ground. Your compiler was the first usable for Nuitka and with little effort.

  • The Scons project

    Thanks for tackling the difficult points and providing a Python environment to make the build results. This is such a perfect fit to Nuitka and a dependency that will likely remain.

  • The valgrind project

    Luckily we can use Valgrind to determine if something is an actual improvement without the noise. And it's also helpful to determine what's actually happening when comparing.

  • The NeuroDebian project

    Thanks for hosting the build infrastructure that the Debian and sponsor Yaroslav Halchenko uses to provide packages for all Ubuntu versions.

  • The openSUSE Buildservice

    Thanks for hosting this excellent service that allows us to provide RPMs for a large variety of platforms and make them available immediately nearly at release time.

  • The MinGW64 project

    Thanks for porting the gcc to Windows. This allowed portability of Nuitka with relatively little effort.

  • The Buildbot project

    Thanks for creating an easy to deploy and use continuous integration framework that also runs on Windows and is written and configured in Python code. This allows to run the Nuitka tests long before release time.

  • The Redbaron project

    Thanks for creating a white space preserving and easy to use toolwork for refactoring Python. This has allows us to automatically format my Python code according to preferences and make global changes easily.

  • The isort project

    Thanks for making nice import ordering so easy. This makes it so easy to let your IDE do it and clean up afterwards.

Updates for this Manual

This document is written in REST. That is an ASCII format which is readable as ASCII, but used to generate PDF or HTML documents.

You will find the current source under:;a=blob_plain;f=README.rst

And the current PDF under: